Tag Archives: MMP16

In and the retinoblastoma (Rb) mutant and mutants and b) up-regulated in the ortholog of acute lymphoblastic leukemia-1 (ALL-1)-fused gene from chromosome 10 (AF10), ZFP-1, or tumor suppressor Rb, to regulate overlapping sets of genes and predicts a large role for RNAi-based chromatin silencing in control of gene expression in encodes the largest number of Argonaute proteins, which interact with short RNAs (1). pathway genes and transcription (7). Two chromatin-related genes, and Rb protein LIN-35 represses inappropriate transcription of germline-specific genes (12) and growth factors (13) in differentiated somatic cells and functions redundantly with other transcriptional repressors (14). Also, mutants are more sensitive to exogenous RNAi than wild-type worms (11, 15). This might be partially because of the de-repression of germline-specific RNAi pathway genes in somatic cells. Because RNAi genes were found to function in the same processes as and mutants affecting RNAi-TGS. RDE-4 is a dsRNA binding protein interacting with Dicer (16) whereas ZFP-1 is a nuclear protein that is likely to affect transcription directly. Our previous study indicated that miRNAs might have a role in promoting RNAi-TGS in as well (6); therefore, we included miRNA pathway Argonaute mutant in our experiments. Our analysis revealed and mutant animals have strikingly similar profiles of alterations in gene expression and and mutants. These genes therefore might represent direct targets of chromatin-based silencing induced by endogenous RNAi pathways. Interestingly, endo-siRNAs matched not only genes negatively regulated by and expression. Our results suggest that ZFP-1 may play both a positive and a negative role in regulating gene expression. Results Microarray Data Analysis. To find target genes regulated by RNAi and Rb, we performed a series of microarray experiments using RNA from L1-L2 buy 340963-86-2 larvae of the wild type and loss-of-function mutants (17), (10), (7), and (18). We conducted pairwise comparisons of the levels of gene expression in each mutant compared with the wild type and selected statistically significant changes in gene expression by two-sample test (value <0.01), requiring in addition an expression difference of at least 1.5-fold between two group averages. Our microarray data are summarized in Dataset S1 and Dataset S2. A majority of the genes changing expression in the mutant compared with the wild type (535 of 710) were up-regulated consistent with the repressive role of the LIN-35 protein (Table 1). Similar numbers of genes were either up-regulated or down-regulated in each of the RNAi-related mutants: 420 were up in and 434 were down whereas 285 were up in and 219 were down, and 170 were up in and 213 were down. The numbers of genes similarly regulated in different mutants are listed in Table 1. Ten genes commonly up-regulated in all four mutants are described in Table S1. Table 1. Numbers of genes changing expression compared with the wild type in indicated mutant backgrounds (top) and numbers of overlapping genes between indicated mutants buy 340963-86-2 (bottom) and Mutants Have Similar Gene Expression Profiles. A comparison of gene sets misregulated in the studied mutants revealed a very significant overlap between genes regulated by and genes regulated by (close to 250) are included in a group affected by genes were divided into forty-five expression clusters (mounts) of coregulated genes. Kim and colleagues also redundantly assigned membership in 56 functional categories to 5, 615 functionally characterized genes, resulting in 8,212 category assignments (19). We mapped our datasets of misregulated genes in various mutants to mounts and categories (Fig. 1). A buy 340963-86-2 heatmap representation with clustering dendrograms summarizing significant enrichment of genes from ours and other relevant studies in functional groups of genes (mounts and categories) defined by Kim and colleagues (19) is shown in Fig. 1 and, more completely, in Fig. S1. In this representation, related functional groups are clustered on the axis and related datasets are buy 340963-86-2 clustered on the axis. This allows functional annotation and comparison of multiple datasets. P-values for statistical significance and representation MMP16 factors for gene enrichment in specific groups.